Born to Design: Innate Human Behaviors Involved in Learning and Practicing Engineering Design
Bibliographic record
Abstract
By researching the existing literature for the abilities and conditions necessary for people to successfully solve engineering design problems, this chapter uncovers a consistent pattern of the cognitive processes involved and explains many of the intrinsic behaviors displayed by designers. Limitations in working memory size explain the use of several design-solution achievement devices: pattern matching; early single-solution conjecture; iteration; co-evolution of problem and solution; and intuition. In addition, learning and creating are found to be similar processes, with both requiring and building upon domain experience, in this case actual designing. Similar too are the processes of seeing and imagining, so that von Helmholtz’s dictum that ‘visual sensations are stronger than acts of the intellect’ can be applied to the solving of engineering design problems. This leads to an explanation for another set of intrinsic designer behaviors: a preference for visualizing solutions (over using abstract analysis); single-solution conjectures; object fixation; and found-object designing. Such explanations should help guide future education and research in design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".